Analytics is the discovery and communication of meaningful patterns in data. Firms may commonly apply analytics to business data to describe, predict, and improve business performance, and a person in an organization may execute analytics queries and obtain relevant data to facilitate decision making. For example, a requester in a department store may execute an analytics query related to profitability analysis of that facility. The confidentiality of analytics queries can avoid leakage of trade secrets and sensitive information related to business decisions. For example, in the scenario above, access to the analytics query may allow internal developers and third parties to predict that the organization may be considering the closing of a store.
Further, analytics queries may include two types of data: certain data and uncertain data. A query causes a company data system to retrieve outputs, and display them to the requester. In most queries, some data will be certain, such as total sales for the previous calendar year, and other data will be uncertain, such as total sales for the next calendar year. Typically, analytics queries do not take into account or process the uncertain data. Further, there is ambiguity in the interpretation of analytics outputs. For example, an analytics query for “electronic chip P123”, may reasonably be interpreted as concerning: i) which devices use the P123 electronic chip or ii) future sales predictions of the P123 electronic chip. Thus, the output of such a query may be ambiguous. Also, the analytics outputs associated with different business scenarios are not generally considered or provided.